ECGWmark: Deep Watermarking for Electrocardiogram Signals
To bridge this gap, we propose ECGWmark, a fully convolutional and quantization-free deep watermarking framework for medical time-series.
ProofPaper ↗
Key points
- Secure transmission of electrocardiogram (ECG) signals remains a major challenge in telecardiology.
- While traditional mathematical watermarking can embed patient metadata directly into ECG signals, it is highly fragile against temporal desynchronization such as circular phase shifts.
- Crucially, we introduce a differentiable Physiological Loss guided by a clinical classifier to preserve diagnostic semantics.
- Experiments on the benchmark MIT-BIH database show that ECGWmark maintains channel-bit extraction accuracy above 90% under the evaluated circular temporal shifts.
Sources (1)
- [1]ECGWmark: Deep Watermarking for Electrocardiogram SignalsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 9, 10:32 AM
To bridge this gap, we propose ECGWmark, a fully convolutional and quantization-free deep watermarking framework for medical time-series.
Secure transmission of electrocardiogram (ECG) signals remains a major challenge in telecardiology.
Extractive summary: sentences quoted from the sources.
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